Towards a European prospective data registry for particle therapy.

The evidence for the value of particle therapy (PT) is still sparse. While randomized trials remain a cornerstone for robust comparisons with photon-based radiotherapy, data registries collecting real-world data can play a crucial role in building evidence for new developments. This Perspective describes how the European Particle Therapy Network (EPTN) is actively working on establishing a prospective data registry encompassing all patients undergoing PT in European centers. Several obstacles and hurdles are discussed, for instance harmonization of nomenclature and structure of technical and dosimetric data and data protection issues. A preferred approach is the adoption of a federated data registry model with transparent and agile governance to meet European requirements for data protection, transfer, and processing. Funding of the registry, especially for operation after the initial setup process, remains a major challenge.

The role of mHealth intervention to improve maternal and child health: A provider-based qualitative study in Southern Ethiopia.

INTRODUCTION: Maternal and child mortality remained higher in developing regions such as Southern Ethiopia due to poor maternal and child health. Technologies such as mobile applications in health may be an opportunity to reduce maternal and child mortality because they can improve access to information. Therefore, the main aim of this study was to explore the role of mHealth in improving maternal and child health in Southern Ethiopia. METHODS: This study employed a qualitative study design to explore the role of mHealth in improving maternal and child health among health professionals in Southern Ethiopia from December 2022 to March 2023. We conducted nine in-depth interviews, six key informants' in-depth interviews, and four focused group discussions among health professionals. This is followed by thematic analyses to synthesize the collected evidence. RESULTS: The results are based on 226 quotations, 5 major themes, and 24 subthemes. The study participants discussed the possible acceptance of mHealth in terms of its fitness in the existing health system, its support to health professionals, and its importance in improving maternal and child health. The participants ascertained the importance of awareness creation before the implementation of mHealth among women, families, communities, and providers. They reported the importance of mHealth for mothers and health professionals and the effectiveness of mHealth services. The participants stated that the main challenges related to acceptance, awareness, negligence, readiness, and workload. However, they also suggested strategic solutions such as using family support, provider support, mothers' forums, and community forums. CONCLUSION: The evidence generated during this analysis is important information for program implementations and can inform policy-making. The planned intervention needs to introduce mHealth in Southern Ethiopia. Planners, decision-makers, and researchers can use it in mobile technology-related interventions. For challenges identified, we recommend solution-identified-based interventions and quality studies.

The effect of mHealth on childhood vaccination in Africa: A systematic review and meta-analysis.

INTRODUCTION: Vaccine-preventable diseases are the public health problems in Africa, although vaccination is an available, safe, simple, and effective method prevention. Technologies such as mHealth may provide maternal access to health information and support decisions on childhood vaccination. Many studies on the role of mHealth in vaccination decisions have been conducted in Africa, but the evidence needs to provide conclusive information to support mHealth introduction. This study provides essential information to assist planning and policy decisions regarding the use of mHealth for childhood vaccination. METHODS: We conducted a systematic review and meta-analysis for studies applying mHealth in Africa for vaccination decisions following the Preferred Reporting Items for Systematic and Meta-Analysis [PRISMA] guideline. Databases such as CINAHL, EMBASE, PubMed, PsycINFO, Scopus, Web of Science, Google Scholar, Global Health, HINARI, and Cochrane Library were included. We screened studies in Endnote X20 and performed the analysis using Revman 5.4.1. RESULTS: The database search yielded 1,365 articles [14 RCTs and 4 quasi-experiments] with 21,070 participants satisfied all eligibility criteria. The meta-analysis showed that mHealth has an OR of 2.15 [95% CI: 1.70-2.72; P<0.001; I2 = 90%] on vaccination rates. The subgroup analysis showed that regional differences cause heterogeneity. Funnel plots and Harbord tests showed the absence of publication bias, while the GRADE scale showed a moderate-quality body of evidence. CONCLUSION: Although heterogeneous, this systematic review and meta-analysis showed that the application of mHealth could potentially improve childhood vaccination in Africa. It increased childhood vaccination by more than double [2.15 times] among children whose mothers are motivated by mHealth services. MHealth is more effective in less developed regions and when an additional incentive party with the messaging system. However, it can be provided at a comparably low cost based on the development level of regions and can be established as a routine service in Africa. REGISTRATION: PROSPERO: CRD42023415956.

Assessing the effect of mHealth on child feeding practice in African countries: systematic and meta-analysis.

INTRODUCTION: Poor child feeding practice is a public health problem in Africa. Mobile health (mHealth) is a supportive intervention to improve this problem; however, the evidence available in the current literature is inconsistent and inconclusive in Africa. Some studies state that exclusive breastfeeding is not different between controls and mHealth interventions in the first month. Other studies state that health providers need additional training for the success of mHealth interventions. OBJECTIVE: This systematic review and meta-analysis aims to provide the summarized effect of mHealth on child-feeding practices in Africa to improve future planning and decisions. METHOD: We conducted a systematic review and meta-analysis based on the published and unpublished evidence gathered from PubMed, Web of Science, Cochrane Library, and Embase databases between January 1, 2000, and March 1, 2022. Studies included were randomized control trials and experimental studies that compared mHealth to standards of care among postpartum women. Preferred Reporting Items for Systematic Review and Meta-analysis guidelines followed for the reporting. RESULTS: After screening 1188 studies, we identified six studies that fulfilled the study criteria. These studies had 2913 participants with the number of total intervention groups 1627 [1627/2913 = 56%]. Five studies were completed within 24 weeks while one required 12 weeks. We included two RCTs, two cluster RCTs, and two quasi-experimental studies all used mHealth as the major intervention and usual care as controls. We found significant improvement in child-feeding practices among intervention groups. CONCLUSION: This systematic review and meta-analysis showed that the application of mHealth improved child-feeding practices in Africa. Although the finding is compelling, the authors recommend high-quality studies and mHealth interventions that consider sample size, design, regional differences, and environmental constraints to enhance policy decisions. The place of residence, access, low socioeconomic development, poor socio-demographic characteristics, low women empowerment, and low women's education might cause high heterogeneity in the included regions and need consideration during interventions. REGISTRATION NUMBER: PROSPERO: CRD42022346950.

Difficult medical encounters in oncology: What physicians need. An exploratory study.

Objective: The objective of this study was to assess how often-medical oncology professionals encounter difficult consultations and if they desire support in the form of training. Methods: In February 2022, a survey on difficult medical encounters in oncology, training and demographics was set up. The survey was sent to 390 medical oncology professionals part of the OncoZON network of the Southeast region of the Netherlands. Results: Medical oncology professionals perceive a medical encounter as difficult when there is a dominant family member (n = 27), insufficient time (n = 24), or no agreement between medical professional and patient (n = 22). Patients involved in these encounters are most often characterized with low health literacy (n = 12) or aggressive behavior (n = 10). The inability to comprehend difficult medical information or perceived difficult behavior complicates encounters. Of the medical oncology professionals, 27-44% preferred a training as a physical group meeting (24%) or an individual virtual meeting (19%). Conclusion: Medical oncology professionals consider dominant or aggressive behavior and the inability to comprehend medical information by patients during consultations as difficult encounters for which they would appreciate support. Innovation: Our results highlight concrete medical encounters in need of specific education programs within daily oncology practice.

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer.

Introduction: Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. Methods: We used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. Results: All models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. Conclusion: The outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model's simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model's predictions is essential.

Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer.

While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve the accuracy of prediction and quality of care. Predicting ED may help aid shared decision-making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis. We used a subset of the ProZIB dataset collected by the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) that contained information on 964 localized prostate cancer cases from 69 Dutch hospitals for model training and external validation. Two models were generated using a logistic regression algorithm coupled with Recursive Feature Elimination (RFE). The first predicted ED 1 year post-diagnosis and required 10 pre-treatment variables; the second predicted ED 2 years post-diagnosis with 9 pre-treatment variables. The validation AUCs were 0.84 and 0.81 for 1 year and 2 years post-diagnosis respectively. To immediately allow patients and clinicians to use these models in the clinical decision-making process, nomograms were generated. In conclusion, we successfully developed and validated two models that predicted ED in patients with localized prostate cancer. These models will allow physicians and patients alike to make informed evidence-based decisions about the most suitable treatment with quality of life in mind.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Purpose: Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Methods: Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). Results: The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. Conclusion: We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Taking shared decision making for prostate cancer to the next level: Requirements for a Dutch treatment decision aid with personalized risks on side effects.

Background: Different curative treatment modalities need to be considered in case of localized prostate cancer, all comparable in terms of survival and recurrence though different in side effects. To better inform patients and support shared decision making, the development of a web-based patient decision aid including personalized risk information was proposed. This paper reports on requirements in terms of content of information, visualization of risk profiles, and use in practice. Methods: Based on a Dutch 10-step guide about the setup of a decision aid next to a practice guideline, an iterative and co-creative design process was followed. In collaboration with various groups of experts (health professionals, usability and linguistic experts, patients and the general public), research and development activities were continuously alternated. Results: Content requirements focused on presenting information only about conventional treatments and main side effects; based on risk group; and including clear explanations about personalized risks. Visual requirements involved presenting general and personalized risks separately; through bar charts or icon arrays; and along with numbers or words, and legends. Organizational requirements included integration into local clinical pathways; agreement about information input and output; and focus on patients' numeracy and graph literacy skills. Conclusions: The iterative and co-creative development process was challenging, though extremely valuable. The translation of requirements resulted in a decision aid about four conventional treatment options, including general or personalized risks for erection, urinary and intestinal problems that are communicated with icon arrays and numbers. Future implementation and validation studies need to inform about use and value in practice.

External validation of a prediction model for timely implementation of innovations in radiotherapy.

BACKGROUND AND PURPOSE: The aim of this study was to externally validate a model that predicts timely innovation implementation, which can support radiotherapy professionals to be more successful in innovation implementation. MATERIALS AND METHODS: A multivariate prediction model was built based on the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) criteria for a type 4 study (1). The previously built internally validated model had an AUC of 0.82, and was now validated using a completely new multicentre dataset. Innovation projects that took place between 2017-2019 were included in this study. Semi-structured interviews were performed to retrieve the prognostic variables of the previously built model. Projects were categorized according to the size of the project; the success of the project and thepresence of pre-defined success factors were analysed. RESULTS: Of the 80 included innovation projects (32.5% technological, 35% organisational and 32.5% treatment innovations), 55% were successfully implemented within the planned timeframe. Comparing the outcome predictions with the observed outcomes of all innovations resulted in an AUC of the external validation of the prediction model of 0.72 (0.60-0.84, 95% CI). Factors related to successful implementation included in the model are sufficient and competent employees, desirability and feasibility, clear goals and processes and the complexity of a project. CONCLUSION: For the first time, a prediction model focusing on the timely implementation of innovations has been successfully built and externally validated. This model can now be widely used to enable more successful innovation in radiotherapy.

Technical note: A knowledge graph approach to registering tumour specific data of patient-candidates for proton therapy in the Netherlands.

The registration of multi-source radiation oncology data is a time-consuming and labour-intensive procedure. The standardisation of data collection offers the possibility for the acquisition of quality data for research and clinical purposes. With this study, we present an overview of the different tumour group data lists in the Dutch national proton therapy registry. Furthermore, as a representative example of the workings of these different tumour-specific knowledge graphs, we present the FAIR (Findable, Accessible, Interoperable, Reusable) data principles-compliant knowledge graph approach describing the head and neck tumour variables using radiotherapy domain ontologies and semantic web technologies. Our goal is to provide the radiotherapy community with a flexible and interoperable data model for data exchange between centres. We highlight data variables that are needed for models used in the model-based approach (MBA), which ensures a fair selection of patients that will benefit most from proton therapy.

Health Literacy and eHealth: Challenges and Strategies.

Given the impact of health literacy (HL) on patients' outcomes, limited health literacy is a major barrier to improve cancer care globally. HL refers to the degree in which an individual is able to acquire, process, and comprehend information in a way to be actively involved in their health decisions. Previous research found that almost half of the population in developed countries have difficulties in understanding health-related information. With the gradual shift toward the shared decision making process and digital transformation in oncology, the need for addressing low HL issues is crucial. Decision making in oncology is often accompanied by considerable consequences on patients' lives, which requires patients to understand complex information and be able to compare treatment methods by considering their own values. How health information is perceived by patients is influenced by various factors including patients' characteristics and the way information is presented to patients. Currently, identifying patients with low HL and simple data visualizations are the best practice to help patients and clinicians in dealing with limited health literacy. Furthermore, using eHealth, as well as involving HL mediators, supports patients to make sense of complex information.

Independent validation of a dysphagia dose response model for the selection of head and neck cancer patients to proton therapy.

Background and purpose: The model based approach involves the use of normal tissue complication models for selection of head and neck cancer patients to proton therapy. Our goal was to validate the clinical utility of the related dysphagia model using an independent patient cohort. Materials and Methods: A dataset of 277 head and neck cancer (pharynx and larynx) patients treated with (chemo)radiotherapy between 2019 and 2021 was acquired. For the evaluation of the model discrimination we used statistical metrics such as the sensitivity, specificity and the area under the receiver operating characteristic curve. After the validation we evaluated if the dysphagia model can be improved using the closed testing procedure, the Brier and the Hosmer-Lemeshow score. Results: The performance of the original normal tissue complication probability model for dysphagia grade II-IV at 6 months was good (AUC = 0.80). According to the graphical calibration assessment, the original model showed underestimated dysphagia risk predictions. The closed testing procedure indicated that the model had to be updated and selected a revised model with new predictor coefficients as an optimal model. The revised model had also satisfactory discrimination (AUC = 0.83) with improved calibration. Conclusion: The validation of the normal tissue complication probability model for grade II-IV dysphagia was successful in our independent validation cohort. However, the closed testing procedure indicated that the model should be updated with new coefficients.

Breast cancer patients' most important quality of life themes for a radiotherapy decision aid.

BACKGROUND AND AIM: The BRASA patient decision aid (BRASA-PtDA) facilitates shared decision making for breast cancer patients (BCPs) facing a radiotherapy treatment decision. During evaluations, patients indicated the wish for quantitative information on side effects. Therefore, this study assessed BCPs opinion on which and how information on side effects should be incorporated in the BRASA-PtDA. METHODS: A workshop was organized with BCPs (n = 9), researchers (n = 5) and clinicians (n = 3). Subsequently, a survey was sent to BCPs (n = 744) investigating the generalisability of the workshop findings, and posing additional questions. The survey entailed multiple choice questions on quality of life themes, the use of a decision aid and risk communication. RESULTS: The workshop revealed BCPs wish for a layered, all encompassing information system. Information on the impact of side effects on daily life was preferred above the risk of these side effects. The survey revealed that important quality of life (QoL) themes were having energy (81%; n = 605), arm function (61%; n = 452), pain (55%; n = 410). Despite the focus on qualitative effects in the workshop, 89% of the survey respondents also wanted to be informed on individualized risks of side effects. 54% Of the survey respondents had never heard of a PtDA. CONCLUSIONS: BCPs preferred information on the impact of side effects, but also their individualized risks on side effects. Most important QoL themes were having enough energy, arm function and pain. Consequently, the BRASA-PtDA should be reshaped, starting with quality of life themes, rather than side effects.

Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making.

BACKGROUND: Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision-making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it. OBJECTIVE: To explore (1) the extent to which patient preferences are taken into consideration in non-small-cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM. DESIGN: Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations. SETTING AND PARTICIPANTS: NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics. RESULTS: We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations. CONCLUSION: Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized. PATIENT OR PUBLIC CONTRIBUTION: This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled 'My Best Treatment' that improves patient-centeredness and the lung cancer patient pathway in the Netherlands.

Practitioners' views on shared decision-making implementation: A qualitative study.

INTRODUCTION: Shared decision-making (SDM) refers to the collaboration between patients and their healthcare providers to make clinical decisions based on evidence and patient preferences, often supported by patient decision aids (PDAs). This study explored practitioner experiences of SDM in a context where SDM has been successfully implemented. Specifically, we focused on practitioners' perceptions of SDM as a paradigm, factors influencing implementation success, and outcomes. METHODS: We used a qualitative approach to examine the experiences and perceptions of 10 Danish practitioners at a cancer hospital experienced in SDM implementation. A semi-structured interview format was used and interviews were audio-recorded and transcribed. Data was analyzed through thematic analysis. RESULTS: Prior to SDM implementation, participants had a range of attitudes from skeptical to receptive. Those with more direct long-term contact with patients (such as nurses) were more positive about the need for SDM. We identified four main factors that influenced SDM implementation success: raising awareness of SDM behaviors among clinicians through concrete measurements, supporting the formation of new habits through reinforcement mechanisms, increasing the flexibility of PDA delivery, and strong leadership. According to our participants, these factors were instrumental in overcoming initial skepticism and solidifying new SDM behaviors. Improvements to the clinical process were reported. Sustaining and transferring the knowledge gained to other contexts will require adapting measurement tools. CONCLUSIONS: Applying SDM in clinical practice represents a major shift in mindset for clinicians. Designing SDM initiatives with an understanding of the underlying behavioral mechanisms may increase the probability of successful and sustained implementation.

A knowledge graph representation of baseline characteristics for the Dutch proton therapy research registry.

Cancer registries collect multisource data and provide valuable information that can lead to unique research opportunities. In the Netherlands, a registry and model-based approach (MBA) are used for the selection of patients that are eligible for proton therapy. We collected baseline characteristics including demographic, clinical, tumour and treatment information. These data were transformed into a machine readable format using the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and resulted in a knowledge graph with baseline characteristics of proton therapy patients. With this approach, we enable the possibility of linking external data sources and optimal flexibility to easily adapt the data structure of the existing knowledge graph to the needs of the clinic.

Radiomics integration into a picture archiving and communication system.

Radiomics is referred to as quantitative imaging of biomarkers used for clinical outcome prognosis or tumor characterization. In order to bridge radiomics and its clinical application, we aimed to build an integrated solution of radiomics extraction with an open-source Picture Archiving and Communication System (PACS). The integrated SQLite4Radiomics software was tested in three different imaging modalities and its performance was benchmarked in lung cancer open datasets RIDER and MMD with median extraction time of 10.7 (percentiles 25-75: 8.9-18.7) seconds per ROI in three different configurations.

Shared decision-making for prophylactic cranial irradiation in extensive-stage small-cell lung cancer: an exploratory study.

BACKGROUND: Prophylactic cranial irradiation (PCI) offers extensive-stage small-cell lung cancer (ES-SCLC) patients a lower chance of brain metastasis and slightly longer survival but is associated with a short-term decline in quality of life due to side-effects. This tradeoff between survival and quality of life makes PCI suitable for shared decision-making (SDM), where patients and clinicians make treatment decisions together based on clinical evidence and patient preferences. Despite recent clinical practice guidelines recommending SDM for PCI in ES-SCLC, as well as the heavy disease burden, research into SDM for lung cancer has been scarce. This exploratory study presents patients' experiences of the SDM process and decisional conflict for PCI. METHODS: Radiation oncologists (n=7) trained in SDM applied it in making the PCI decision with ES-SCLC patients (n=25). We measured patients' preferred level of participation (Control Preferences Scale), the level of SDM according to both groups (SDM-Q-9 and SDM-Q-Doc), and patients' decisional conflict [decisional conflict scale (DCS)]. RESULTS: Seventy-nine percent of patients preferred a collaborative role in decision-making, and median SDM scores given by patients and clinicians were 80 (IQR: 75.6-91.1) and 85.2 (IQR: 78.7-88.9) respectively, indicating satisfaction with the process. However, patients experienced considerable decisional conflict. Over 50% lacked clarity about which choice was suitable for them and were unsure what to choose. Sixty-four percent felt they did not know enough about the harms and benefits of PCI, and 60% felt unable to judge the importance of the harms/benefits in their life. CONCLUSIONS: ES-SCLC patients prefer to be involved in their treatment choice for PCI but a substantial portion experiences decisional conflict. Better information provision and values clarification may support patients in making a choice that reflects their preferences.

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers.

Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.